玉米籽粒重金属铅(Pb2+)含量的QTL定位

铅(Pb²⁺)是现存环境最大量的有毒重金属污染元素,在我国特别是西南地区种植的玉米受重金属Pb²⁺污染日益严重,已严重影响到食物安全。文章利用玉米籽粒Pb²⁺低富集自交系178和籽粒Pb²⁺高富集自交系9782杂交衍生的重组自交系群体为作图群体,利用165对SSR多态性标记,构建了总长度为1499.85 cM、标记间平均距离为9.07 cM的分子遗传图谱,对玉米籽粒Pb²⁺含量性状进行了QTL定位分析,以期为选育籽粒低富集Pb²⁺的玉米新品种提供参考。结果表明,在Pb²⁺浓度为333.32 mg/kg胁迫下,共检测到2个与籽粒Pb²⁺含量相关的QTL,分别位于玉米第1、第4号染色体,其中qPC1位于标记区间umc1661~phi002h之间,表型贡献率为11.13%,加性效应为0.062;qPC4位于umc1117~nc005之间,表型贡献率为5.55%,加性效应为-0.044。性状相关分析结果表明,籽粒中Pb²⁺含量与穗长、穗粗、行粒数、穗重和百粒重等产量性状间均未达到显著水平,表明选育玉米籽粒Pb²⁺低富集的新自交系或杂交种不一定会影响到产量性状,而且籽粒Pb²⁺含量是一个相对独立的遗传性状。     

水稻糙米高蛋白基因的QTL定位

利用由糙米蛋白含量高达14．55％的广东省农家品种三春种配制的BC1群体进行糙米高蛋白基因的QTL定位,定位到了6个糙米蛋白基因的QTLs,其中有3个新的QTLs。有1个增效基因qCP1-2为主效基因,解释的表型变异高达44．2％,有可能是与谷蛋白基因Glu-1紧密连锁的新调控序列。本研究表明,所利用的BC1群体的糙米蛋白可能主要是由1个主效QTL所控制。     

水稻籽粒锌含量的QTL定位

锌元素的营养失衡已成为影响人类健康的最重要因素之一,籽粒锌含量的QTL（quantitative trait loci）定位对研究富锌水稻的遗传育种具有重要的意义。以水稻（Oryzasativa L．）亲本奉新红米和明恢100杂交的145个株系的F2群体为实验材料,利用92个SSR（simple sequence repeat）标记对水稻籽粒锌含量进行了QTL定位,共检测到3个QTLs,分别定位于第3、6和11染色体上,对表型变异的贡献率分别为4．97％、12．75％和7．74％。其中位于第3染色体上的分子标记RM186和RM168之间的QZN3对表型变异的贡献率最大,其增效等位基因来自亲本明恢100,表现为部分显性。3个QTLs的联合贡献率为25．46％。具有基因累加效应。该研究结果有利于深入理解水稻锌含量的遗传基础,为锌含量的QTL精细定位、基因克隆和分子标记辅助选择提供依据。     

水稻籽粒锌含量的QTL 定位

锌元素的营养失衡已成为影响人类健康的最重要因素之一, 籽粒锌含量的QTL(quantitative trait loci)定位对研究富锌水稻的遗传育种具有重要的意义。以水稻(Oryza sativa L.)亲本奉新红米和明恢100杂交的145个株系的F2群体为实验材料, 利用92个SSR(simple sequence repeat)标记对水稻籽粒锌含量进行了QTL定位, 共检测到3个QTLs , 分别定位于第3、6和11染色体上, 对表型变异的贡献率分别为4.97%、12.75%和7.74%。其中位于第3染色体上的分子标记RM186和RM168之间的QZN3对表型变异的贡献率最大, 其增效等位基因来自亲本明恢100, 表现为部分显性。3个QTLs 的联合贡献率为25.46%, 具有基因累加效应。该研究结果有利于深入理解水稻锌含量的遗传基础, 为锌含量的QTL精细定位、基因克隆和分子标记辅助选择提供依据。     

水稻QTL分析的研究进展

水稻许多重要的性状是由多基因控制的数量性状,经典的数量遗传学只能把数量性状作为一个整体进行研究。近年来．高密度分子标记连锁图的构建和有效的生物统计学方法的发展使人们对数量性状遗传基础的研究出现了革命性的变化。通过对不同群体内的个体或品系的分子标记基因型和表型数据的共分离分析,能对QTL进行检测和定位。本文对QTL定位的原理和方法进行了介绍,从QTL的数目和效应、上位性效应、QTL基因型与环境的互作、相关性状的QTL以及个体发育不同阶段的QTL等方面对水稻QTL分析的研究进展进行了综述。水稻基因组测序计划已经完成,本文还对基因组时代水稻QTL精细定位和克隆的方法进行了探讨,对QTL分析在水稻育种中的应用前景进行了展望。     

水稻纹枯病抗性QTL分析

对灿稻窄叶青8号（ZYQ8）和粳稻京系17（JX17）以及由它们构建的加倍单倍体（DH）群体,分别在杭州和海南岛,采用注射器接种法进行纹枯病抗性鉴定,并使用该群体的分子链锁图谱进行数量性状座位（QTL）分析。共检测到4个抗纹枯病的QTL（qSBR-2、qSBR-3、qSBR-7和qSBR-11）,分别位于第2、第3、第7和第11染色体。其中qSBR-2、qSBR-3、qSBR-7的抗性基因由抗病亲本ZYQ8贡献,而qSBR-11的抗性基因来自感病亲本JX17。qSBR-2、qSBR-3、qSBR-7在杭州和海南岛都能检测到,而qSBR-11只在杭州检测到。在杭州的实验中,纹枯病病级与秆长和抽穗期呈显著负相关;在控制秆长和抽穗期的QTL中,控制秆长的qCL-3与qSBR-3位于同一染色体区域,其余QTL与抗纹枯病的QTL之间无连锁关系。     

水稻产量相关QTL研究现状

产量是最为复杂的数量性状,对它的遗传机理了解甚微。近15年来,许多学者利用随机分离群体定位了许多影响水稻产量及其组分的QTL,即以QTL定位的方法对产量潜力进行遗传剖析。试验证明上位性效应对产量及其组分性状遗传变异起着重要作用,但目前大多数QTL研究仍侧重于发掘和克隆单个主效QTL,然而对单一基因／QTL的深入了解还不足以诠释复杂性状遗传基础的全貌,还没有为育种家提供足够的可应用于分子标记辅助育种的遗传信息并用于提高水稻产量。笔者认为今后的数量性状研究尚需加强复杂性状QTL遗传网络的发掘,在改良水稻品种性状的同时发展并完善QTL研究。     

Cd胁迫对杂交水稻及其亲本叶片丙二醛含量的影响

以15份杂交水稻及其亲本为材料,采用水培试验研究了不同Cd2＋浓度胁迫对水稻叶片丙二醛（MDA）含量的影响。结果表明：（1）在不同Cd处理水平和生育时期,保持系叶片MDA含量存在差异,如D83B叶片MDA含量各处理显著高于对照（p〈0.05）,MDA积累水平较高,受到Cd严重胁迫,而Ⅱ-32B在两个生育时期受低Cd浓度的影响不大,与对照差异不显著,且随着Cd浓度的增大,MDA含量均表现出较低的MDA积累水平,因此保持系Ⅱ-32B是一种较好的保持系材料。（2）Cd胁迫对恢复系各材料叶片MDA的影响不同,其中以R527、R498叶片MDA积累最明显,孕穗期和灌浆期各处理均显著高于对照,其余恢复系材料受不同Cd2＋浓度的影响,MDA积累不稳定。（3）同一保持系（Ⅱ-32B）与不同恢复系（R498、R549、R892）组合配制的3个杂交稻叶片在MDA含量的增加幅度上表现不同,其中Ⅱ优498、Ⅱ优892受Cd影响较小,无论在孕穗期还是灌浆期叶片MDA含量增幅相对较小,而Ⅱ优549受Cd严重胁迫,各处理均显著高于对照,MDA积累增幅最大。（4）同一恢复系（R498）与不同保持系（Ⅱ-32B、D62B）配制的杂交稻叶片MDA含量亦不同,如D优498仅在孕穗期低浓度下MDA较对照有所降低,其余各处理MDA均积累较多,而Ⅱ优498MDA含量随着Cd浓度的增加,MDA增幅相对较小,可见水稻亲本对子代MDA的积累存在差异,可根据优良亲本,寻找优势组合,培育出抗Cd性强的杂交水稻。     

水稻芽期耐冷性的QTL分析

本研究以98个Nipponbare/Kasalath//Nipponbare回交重组自交家系(backcross-inbred lines,BILs)组成的群体为材料,进行水稻芽期耐冷性数量性状基因座的检测和遗传效应分析.25℃正常条件下水稻发芽7 d,芽长5～10 cm,5℃低温处理10d,之后升温至25℃,缓苗10d,调查活苗率,并以活苗率作为芽期耐冷性的表型值,分析亲本和98个BILs的芽期耐冷性表现.采用Windows QTL Cartographer 1.13a软件的复合区间作图法,共检测到4个苗期耐冷性数量性状基因座(quantative trait locus,QTL),分别位于第3、第7和第12染色体上,命名为qSCT-3-1、qSCT-3-2、qSCT-7和qSCT-12.4个QTL的加性效应分别为11.16、11.14、-8.8和-14.59,可解释表型变异的12.11%,12.66%,6.82%和15.86%.     

粳稻糙米钙含量QTL分析

以'十和田'为轮回亲本,'丽粳2号'为供体,培育出糙米钙含量近等基因系下的重组近交系261个BC_5F_6株系,采用BSA法从遍布水稻12条染色体上的600对SSR引物中筛选到1个与糙米钙含量有关联的SSR标记RM5536.进一步据其在水稻染色体上的位置,结合PCR找到了与糙米钙含量有关的3个SSR标记(RM5794、RM5362和RM12178).用MAPMAKER/EXP3.0软件做出了这4个标记的连锁群,最后采用混合线性模型找到了糙米钙含量QTL位点.QTL分析结果显示:该位点位于1号染色体引物RM12178和RM5362之间,贡献率为9.62%,为新发现的糙米钙含量QTL位点,暂命名为qBRCA-1.并发现与糙米锶含量有关的QTL位点,其位于标记RM5362和RM5794之间,贡献率为3.93%.同时本试验首次从分子水平上验证了偏相关比简单相关更准确解释元素间的相关性.     

Mapping QTLs of root morphological traits at different growth stages in rice

Roots are a vital organ for absorbing soil moisture and nutrients and influence drought resistance. The identification of quantitative trait loci (QTLs) with molecular markers may allow the estimation of parameters of genetic architecture and improve root traits by molecular marker-assisted selection (MAS). A mapping population of 120 recombinant inbred lines (RILs) derived from a cross between japonica upland rice 'IRAT109' and paddy rice 'Yuefu' was used for mapping QTLs of developmental root traits. All plant material was grown in PVC-pipe. Basal root thickness (BRT), root number (RN), maximum root length (MRL), root fresh weight (RFW), root dry weight (RDW) and root volume (RV) were phenotyped at the seedling (I), tillering (II), heading (III), grain filling (IV) and mature (V) stages, respectively. Phenotypic correlations showed that BRT was positively correlated to MRL at the majority of stages, but not correlated with RN. MRL was not correlated to RN except at the seedling stage. BRT, MRL and RN were positively correlated to RFW, RDW and RV at all growth stages. QTL analysis was performed using QTLMapper 1.6 to partition the genetic components into additive-effect QTLs, epistatic QTLs and QTL-by-year interactions (Q x E) effect. The results indicated that the additive effects played a major role for BRT, RN and MRL, while for RFW, RDW and RV the epistatic effects showed an important action and Q x E effect also played important roles in controlling root traits. A total of 84 additive-effect QTLs and 86 pairs of epistatic QTLs were detected for the six root traits at five stages. Only 12 additive QTLs were expressed in at least two stages. This indicated that the majority of QTLs were developmental stage specific. Two main effect QTLs, brt9a and brt9b, were detected at the heading stage and explained 19% and 10% of the total phenotypic variation in BRT without any influence from the environment. These QTLs can be used in breeding programs for improving root traits.     

Comparison of QTLs for rice seedling morphology under different water supply conditions

The variation of seedling characteristics under different water supply conditions is strongly associated with drought resistance in rice (Oryza sativa L.) and a better elucidation of its genetics is helpful for improving rice drought resistance. Ninety-six doubled-haploid (DH)rice lines of an indica and japonica cross were grown in both flooding and upland conditions and QTLs for morphological traits at seedling stage were examined using 208 restriction fragment length polymorphism (RFLP) and 76 microsatellite (SSR) markers. A total of 32 putative QTLs were associated with the four seedling traits: average of three adventitious root lengths (ARL), shoot height (SH), shoot biomass (SW), and root to shoot dry weight ratio (RSR). Five QTLs detected were the same under control and upland conditions. The ratio between the mean value of the seedling trait under upland and flooding conditions was used for assessing drought tolerance. A total of six QTLs for drought tolerance were detected. Comparative analysis was performed for the QTLs detected in this case and those reported from two other populations with the same upland rice variety Azucena as parent. Several identical QTLs for seedling elongation across the three populations with the positive alleles from the upland rice Azucena were detected, which suggests that the alleles of Azucena might be involved in water stress-accelerated elongation of rice under different genetic backgrounds. Five cell wall-related candidate genes for OsEXP1, OsEXP2, OsEXP4, EXT, and EGase were mapped on the intervals carrying the QTLs for seedling traits.     

The auxin transporter,OsAUX1, is involved in primary root and root hair elongation and in Cd stress responses in rice (Oryza sativa L.)

Auxin and cadmium (Cd) stress play critical roles during root development. There are only a few reports on the mechanisms by which Cd stress influences auxin homeostasis and affects primary root (PR) and lateral root (LR) development, and almost nothing is known about how auxin and Cd interfere with root hair (RH) development. Here, we characterize rice osaux1 mutants that have a longer PR and shorter RHs in hydroponic culture, and that are more sensitive to Cd stress compared to wild‐type (Dongjin). OsAUX1 expression in root hair cells is different from that of its paralogous gene, AtAUX1, which is expressed in non‐hair cells. However, OsAUX1, like AtAUX1, localizes at the plasma membrane and appears to function as an auxin tranporter. Decreased auxin distribution and contents in the osaux1 mutant result in reduction of OsCyCB1;1 expression and shortened PRs, LRs and RHs under Cd stress, but may be rescued by treatment with the membrane‐permeable auxin 1‐naphthalene acetic acid. Treatment with the auxin transport inhibitors 1‐naphthoxyacetic acid and N‐1‐naphthylphthalamic acid increased the Cd sensitivity of WT rice. Cd contents in the osaux1 mutant were not altered, but reactive oxygen species‐mediated damage was enhanced, further increasing the sensitivity of the osaux1 mutant to Cd stress. Taken together, our results indicate that OsAUX1 plays an important role in root development and in responses to Cd stress.     

水稻籼粳交DH群体收获指数及源库性状的QTL分析

以 1个水稻籼粳交 (圭 6 30 0 2 4 2 8)来源的DH群体为材料 ,利用 1张含有 2 32个标记的RFLP连锁图谱和基于混合线性模型的定位软件QTLMapper1 0对水稻收获指数及生物量、籽粒产量、库容量和株高 5个性状进行QTL分析 ,共检测到 2 1个主效应QTLs和 9对上位性互作位点。其中 ,控制籽粒产量的 3个QTLs合计贡献率为 4 2 % ,LOD值为 7 10 ;这 3个QTLs或者与收获指数的QTL同位 ,或者与生物量的QTL同位 ,且加性效应的方向一致 ,从而揭示了“籽粒产量 =生物量×收获指数”的遗传基础所在。控制收获指数的 4个QTLs合计贡献率为 4 6 % ,LOD值为 10 3;控制生物量的 4个QTLs合计贡献率为 6 4 % ,LOD值为 14 0 9;收获指数的 4个QTLs与生物量的 4个QTLs均不同位。因此 ,通过基因重组 ,可能实现控制收获指数和生物量的增效基因的聚合 ,由此获得收获指数和生物量“双高”的基因型。检测到 5个株高QTLs,其合计贡献率为 6 4 % ,LOD值为 11 6 2 ;其中 ,有 3个效应较小的QTLs与生物量、库容量和 或籽粒产量QTLs同位 ,且同位QTLs的加性效应方向一致 ;未发现株高QTLs与收获指数QTLs的同位性。由此表明 ,株高与“源 流 库”概念中的“源”和“库”在遗传上有一定程度的关联 ,而与“流”无关联。此外还发现 ,在上述同位性QTL     

Comparative study of QTLs for agronomic traits of riceOriza sativa L.) between salt stress and nonstress environment

Genotype-by-environment interactions (GxE) are commonly observed for quantitative traits. In the present study, a doubled haploid (DH) population and its genetic linkage map were used to comparatively study QTLs in salt stress and nonstress environments. A total of 24 QTLs were detected for five agronomic traits, which were distributed on all the chromosomes except 9 and 11. Under the salt stress, nine (37.5%) QTLs were detected, including one for 1 000-grain weight (GW), two for heading date (HD), one for plant height (PH), two for grains per panicle (GPP), and three for effective tillers (ET), while in the nonstress environment, 17 QTLs (70.8%) were detected, including five for GW, six for HD, three for PH, two for GPP, and one for ET. Two QTLs (8.3%) were consistently detected in both environments. One was identified on chromosome 4 for HD and the other on Chr.6 for GPP. Furthermore, three regions carrying multiple QTLs were identified on chromosomes 1, 4 and 8 respectively. For example, on chromosome 8, three QTLs for HD, GW and PH, respectively were identified between RG885-GA408 in nonstress environment, but not in the stress environment. The comparative study of QTLs detected in extremely different (salt stress and nonstress) environments revealed that there existed several QTLs for important agronomic traits on chromosome 8 which were affected significantly by salt stress.     

不同遗传背景及环境中水稻（Oryza sativa L.）穗长的QTLs和上位性分析

以粳稻Ａｚｕｃｅｎａ为父本与灿稻ＩＲ６４杂交发展的一双单倍体（ＤＨ） 本,与灿稻ＩＲ１５５２杂交发展的一重组自交系（ＲＩ）群体为材料,应用分子标记图说对２个群体在大田答舅栽２个环境下的穗长进行ＱＴＬｓ及上位性效应分析,ＤＨ群体中共检测６个穗长ＱＴＬｓ,位于第１、４长染色体上的３个ＱＴＬｓ,,在２个环境中稳定表达,未检测一闰性效应,加性效应为穗长遗传主效应,ＲＩ群体中,共检测到３个穗长ＱＴＬｓ及６对     

Developmental behavior of gene expression for brown rice thickness under different environments

The dynamic changes of genetic effects, including main effects, and genotype x environment (GE) interaction effects on brown rice thickness (BRT) across environments were investigated by using the developmental genetic models. Seven cytoplasmic male sterile lines of indica rice (Oryza sativa L.) as females and five restoring lines as males were used in a factorial design to produce grains of F(1)s and F(2)s in two environments (years) for developmental genetic analysis. The results indicate that genetic effects, especially GE interaction effects of triploid endosperm genes, cytoplasm genes, and diploid maternal plant genes were important to the performance of BRT at various filling stages of rice. The BRT was genetically controlled by the net genetic effects of genes expressed at the early and late filling stages (1-7 days and 15-21 days after flowering, respectively). The differences in net genetic effects under different environments for endosperm, cytoplasm, and maternal plant genes were found, and the net GE interaction effects were more important to BRT at the early filling and mature stages of rice. Some net genetic effects, especially for net cytoplasm effects spasmodically expressed, were detected among filling stages. Higher additive and cytoplasm main effects, along with their interaction effects, were found, which would be useful for selection for BRT in breeding programs. The predicated genetic effects at different filling stages show that the parents of V20 and Xieqingzao were better than others for improving BRT of progenies.     

Spectral response of rice (Oryza sativa L.) leaves to Fe2+ stress

In the management of lake eutrophication, the regulation effect of Fe is considered, in addition to the controlling nitrogen- and phosphorus input. Based on the “Fe hypothesis”, this paper tentatively ap-plied plant spectral response to the remote sensing early-warning mechanism of lake eutrophication. A laboratory water culture experiment with rice (Oryza sativa L.) was conducted to study Fe uptake by plants and the chlorophyll concentration and visible-near infrared spectrum of vegetable leaves as well as their interrelations under Fe²⁺ stress. Three spectral indices, i.e., A₁ (integral value of the changes of spectral reflectivity in the range 460―670 nm under Fe²⁺ stress), A₂ (integral value of the changes of spectral reflectivity in the range of 760―1000 nm under Fe²⁺ stress) and S (blue-shift range of red edge curve under Fe²⁺ stress), were used to establish quantitative models about the relationships between the rice leaf spectrum and Fe²⁺ stress. With the increase of Fe²⁺ in a culture solution, the Fe content in rice plants increased, while the chlorophyll concentration in vegetative leaves decreased. The spectral reflectivity of vegetable leaves increased in the visible light band but decreased in the near infrared band, and the blue-shift range of the red edge curve increased. The indices _A1, A₂ and S all had sig-nificant correlations with the Fe content in rice leaves, the correlation coefficient being respectively 0.951 (P < 0.01), −0.988 (P < 0.01) and 0.851 (P < 0.01), and simulated (multiple correlation coefficients R² > 0.96) and predict the Fe level in rice leaves.     

Comparative study of QTLs for agronomic traits of rice (Oriza sativa L.) between salt stress and nonstress environment

Genotype-by-environment interactions (GxE) are commonly observed for quantitative traits. In the present study, a doubled haploid (DH) population and its genetic linkage map were used to comparatively study QTLs in salt stress and nonstress environments. A total of 24 QTLs were detected for five agronomic traits, which were distributed on all the chromosomes except 9 and 11. Under the salt stress, nine (37.5%) QTLs were detected, including one for 1 000-grain weight (GW), two for heading date (HD), one for plant height (PH), two for grains per panicle (GPP), and three for effective tillers (ET), while in the nonstress environment, 17 QTLs (70.8%) were detected, including five for GW, six for HD, three for PH, two for GPP, and one for ET. Two QTLs (8.3%) were consistently detected in both environments. One was identified on chromosome 4 for HD and the other on Chr.6 for GPP. Furthermore, three regions carrying multiple QTLs were identified on chromosomes 1, 4 and 8 respectively. For example, on chromosome